臺灣博碩士論文加值系統

葉綠素含量是植物健康狀況和重要生物物理參數的良好指標，對於精準農業具有重要意義。使用葉綠素計的傳統方法需要很多點樣本來估算田間葉綠素含量之空間變異性。由於某些波段的葉綠素含量和光譜反射率之間的關係，遙測技術具有預測大田間的葉綠素含量的潛力。在這項研究中，使用無人載具(UAV)所取得的多光譜解析圖像利用迴歸模式來選擇對葉綠素含量反應敏感的植被指數。此研究的目的是調查多光譜相機用於估算葉綠素含量的性能。遙感技術在水稻上的應用進行在1月至5月的6個時期，所有的生長期間都有四個窄波段傳感器(綠光、紅光、紅邊和近紅外光) 用以估算葉綠素含量。共有9個生理指數被決定為估計葉綠素含量。常態植被指數 (NDVI)、多時相植被指數(MTVI)、常態綠紅差異指數(NGRDI)、紅邊常態植被指數(REGNDVI)、葉綠素植被指數(CVI)被發現在田間的葉綠素含量測定是準確的和線性估計量。

Chlorophyll content, a good indicator for plant healthy state and important biophysical parameters, is important significance for precision agriculture. To estimate the spatial variability of chlorophyll content over fields, traditional method using chlorophyll meter requires many point samples. Because of relationship between chlorophyll content and spectral reflectance of certain bands, remote sensing techniques have the potential to predict the chlorophyll content over large fields. In this study, the use of multispectral resolution imagery using unmanned aerial vehicle called UAV is to select the vegetation indices sensitive to chlorophyll content using regression model. The goal of our study is to investigate the performance of multispectral camera for estimation of chlorophyll content. The application of remote sensing techniques on paddy rice were conducted on six dates from January to May during all stage growth with four narrow band sensors
(Green, Red, Red Edge and Near Infrared) in order to estimate the chlorophyll content. Nine physiological indices were determined to estimate the chlorophyll content. Normalized Vegetation index (NDVI), Modified Triangular vegetation index (MTVI), Normalized Green-Red Difference Index (NGRDI), Red Edge NDVI (REGNDVI), Chlorophyll Vegetation Index (CVI) were found to be accurate and linear estimators of chlorophyll content measured in the fields.

Table of contents

摘要 I
Abstract II
Acknowledgements IV
Table of contents VI
List of Tables VIII
List of Figures IX
Chapter I: Introduction 1
1.1 Background of the study 1
1.2 Research objective 5
Chapter II: Literature Review 6
2.1 Unmanned aerial vehicle or systems (UAS or UAV) for precision agriculture 6
2.2 Type of UAV 7
2.2.1 Multi rotor 7
2.2.2 Fixed-Wing 8
2.2.3 Single-Rotor Helicopter 9
2.3 Importance of Chlorophyll in the plant 11
2.4 Reasons to measure chlorophyll content 12
2.5 Estimation of chlorophyll content 13
2.5.1 Traditional Method 13
2.5.2 SPAD 502 chlorophyll meter 17
2.5.3 Remote sensing of Chlorophyll content 18
Chapter III: Materials and methods 20
3.1 Study area 20
3.2 Field data measurements for leaf chlorophyll content 21
3.3 UAV platform Description 22
3.4 Sequoia multispectral sensor bands data acquisition 23
3.5 Calibration Target 24

3.6 UAV Data processing 26
3.6.1 Ortho-rectification and Orthomosaic generation 26
3.6.2 Radiometric Calibration 26
3.6.3 Ground coordinates of sampled chlorophyll coincided precisely 28
3.6.4 Vegetation indices used in this analysis 28
3.7 Analytical method 32
3.7.1 Stepwise regression Model 32
3.7.1.1 Forward stepwise selection of variables 33
3.7.1.2 Backward elimination of variables 33
3.7.1.3 Stepwise selection 33
3.7.1.4 Stopping Rules 34
Chapter IV: Results and Discussion 36
4.1 Statistical description of ground truth data 36
4.1.1 The general linear model (GLM) Procedure 36
4.1.2 Variation of Chlorophyll content over weeks and water treatment 37
4.1.3 Chlorophyll content and water treatment. 40
4.2 Relation between chlorophyll content calculated indices for different water treatments 41
4.2.1 Calculation of different indices maps 41
4.2.2 Linear relationship between chlorophyll content measured and calculated indices 45
4.3 Prediction of chlorophyll content using stepwise model 51
4.3.1 Predictors for the four-water treatment using stepwise 51
4.3.2 Correlation and multiple regression analyses 54
Chapter V: Conclusion 59
References 61
Bio-sketch of Author 72
 
List of Tables

Table 1. Physical characteristics and safety consideration of seven extraction solvent. 16
Table 2. Specifications of the Multi Spectral camera 24
Table 3. Various remote sensing indices related to chlorophyll content canopy foliage content, gap fraction, and senescence 31
Table 4. Variables of stepwise regression model 32
Table 5. General linear model of chlorophyll content, week and water treatment. 37
Table 6. Variation of chlorophyll content over weeks 38
Table 7. Variation of chlorophyll content and water depth 40
(chlorophyll versus water treatment) 40
Table 8. Various indices and coefficient by ascending order for four treatments 51
Table 9. Variables or predictors used in each water treatment 52
Table 10. Analyze of Variance of four model for the four water treatments 53
Table 11. Coefficients to the model of each water treatment 56
Table 12. Model summary 57

 
List of Figures
Figure 1. Multi rotor UAV (This study) 10
Figure 2. AggieAir air frame Layout (Elarab et al., 2015), 10
Figure 3. Single-Rotor Helicopter (https://www.x20.org/exocet-vtol-uav-uas-unmanned-drone) 10
Figure 4. Fixed wing sequoia (https://unmanned-aerial.com/senseflys) 10
Figure 5. Study area 21
Figure 6. Experimental design in the Paddy Fields 21
Figure 7. 3DR Solo air frame 23
Figure 8. Relative spectral response 25
Figure 9. Calibration concept 25
Figure 10. Calibration panel 25
Figure 11. Radiometric calibration Process 27
Figure 12. Images mosaicking and orthorectification 27
Figure 13. Interaction between water-treatments of chlorophyll (chl) content over the six weeks 39
Figure 14. Variation of chlorophyll (chl) content over the six weeks 39
Figure 15. Variation of chlorophyll (chl) content by treatment 40
Figure 16. Different vegetation indices maps 43
Figure 17. Scatter plots indicating the relationship between various indices and chlorophyll concentration index (CCI) T2cm 47
Figure 18. Scatter plots indicating the relationship between various indices and chlorophyll concentration index (CCI) T3cm 48
Figure 19. Scatter plots indicating the relationship between various indices and chlorophyll concentration index (CCI) T4cm 49
Figure 20. Scatter plots indicating the relationship between various indices and chlorophyll concentration index (CCI) T5cm 50

References
Andrew, D. R., P. D. Shane, and P. B. Graeme. 2001. An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytologist: 10 pages.
Arnon, D. I. 1949. Copper enzymes in isolated chloroplasts. Polyphenoloxidase in beta vulgaris. Plant Physiology, Volume 24
Azia, F., and K. A. Stewart. 2007. Relationships between extractable chlorophyll and SPAD values in muskmelon leaves. Plant Nutrition, Volume 24, 2001 - Issue 6: Pages: 961-966.
Berk, K. N. (1978). Comparing subset regression procedures. Technometrics, (Vol. 20:1–6.).
Blackmer, T. M., and J. S. Schepers. 1995. Use of a chlorophyll meter to monitor nitrogen status and schedule fertigation for corn. J Prod Agric, 8. http://dx.doi.org/10.2134/jpa1995.0056
Broge, N. H., and E. Leblanc. 2001. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76(2):156-172.
 
Cabangon, R. J., E. G. Castillo, and T. P. Tuong. 2011. Chlorophyll meter-based nitrogen management of rice grown under alternate wetting and drying irrigation. Field Crops Res, 121.
Carrijo, D. R., M. E. Lundy, and B. A. Linquist. 2017. Rice yields and water use under alternate wetting and drying irrigation: A meta-analysis. Field Crops Research, 203: 173-180.
Castro, F. A., E. Campostrini, A. T. Netto, and L. H. Viana. 2012. Relationship between photochemical efficiency (JIP-Test Parameters) and portable chlorophyll meter readings in papaya plants  Brazilian Society of Plant Physiology, Braz. J. Plant Physiol., 23(4): 295-304
Chander, G., B. L. Markham, and D. L. Helder. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5): 893-903.
Clevers, J. G. P. W., and A. A. Gitelson. 2013. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. International Journal of Applied Earth Observation and Geoinformation, 23: 344-351.
Curran, P. J., J. L. Dungan, and H. L. Gholz. 1990. Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. Tree Physiology 7,33-48.
 
Daughtry, C. S. T., C. L. Walthall, M. S. Kim, E. B. de Colstoun, and J. E. McMurtrey Iii. 2000. Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sensing of Environment, 74(2): 229-239.
Dean, J., J. Mixter, and J. Barr. (2015). Multi-Rotor Unmanned Aerial Vehicle. Western Michigan University ScholarWorks at WMU.
Elarab, M., A. M. Ticlavilca, A. F. Torres-Rua, I. Maslova, and M. McKee. 2015. Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture. International Journal of Applied Earth Observation and Geoinformation, 43:32-42.
Gates, D. M., H. J. Keegan, J. C. Schleter, and V. R. Weidner. 1965. Spectral Properties of Plants. Applied Optics, 4(1):11-20.
Gitelson, A., and M. N. Merzlyak. 1994. Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation. Journal of Plant Physiology, 143(3): 286-292. http://www.sciencedirect.com/science/article/pii/S0176161711816330.
Gitelson, A. A., Y. Gritz , and M. N. Merzlyak. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3): 271-282.
 
Gitelson, A. A., and M. N. Merzlyak. 1998. Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research, 22(5): 689-692.
Gitelson, A. A., M. N. Merzlyak, and Y. Grits. 1996. Novel algorithms for remote sensing of chlorophyll content in higher plant leaves. Geoscience and Remote Sensing Symposium, (4): 2355-2357.
Guo, T., T. Kujirai, and T. Watanabe. 2012. Mapping crop status from an unmanned aerial vehicle for precision agriculture applications. Remote Sensing and Spatial Information Sciences, XXXIX-B1.
Haboudane, D., J. R. Miller, E. Pattey, P. J. Zarco-Tejada, and I. B. Strachan. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3): 337-352.
Haboudane, D., J. R. Miller, N. Tremblay, P. J. Zarco-Tejada, and L. Dextraze. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2–3): 416-426.
Haboudane, D., N. Tremblay, J. R. Miller, and P. Vigneault. 2008. Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Transactions On Geoscience And Remote Sensing, Vol. 46, No. 2.  
Harman, S. 2017. Characteristics of the Radar signature of multi-rotor UAVs. Paper presented at the 2016 European Radar Conference (EuRAD).
Kayacan, E., M. A. Khanesar, J. Rubio-Hervas, and M. Reyhanoglu. 2017. Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks. International Journal of Aerospace Engineering, Volume 2017, Article ID 5402809: 12 pages.
Kim, M. S., C. S. T. Daughtry, E. W. Chappelle, J. E. Mcmurtrey, and C. L. Walthall. 1994. The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation. Conference Paper: 299-306..
Krishnamurthy, P., and F. Khorrami. 2015. A Control Design for Quad Rotor UAVs with Input Unmodeled Dynamics. IFAC-PapersOnLine, 48(11): 227-232. http://www.sciencedirect.com/science/article/pii/S2405896315012690
Lampayan, R. M., R. M. Rejesus, G. R. Singleton, and B. A. M. Bouman. 2015. Adoption and economics of alternate wetting and drying water management for irrigated lowland rice. Field Crops Research, 170: 95-108.http://www.sciencedirect.com/science/article/pii/S0378429014003001
Larcher, W. 1995. Physiological Plant Ecology, . Springer, 3rd edn. Berlin, Germany.
Lichtenthaler, H. K. 1987. Chlorophylls and Carotenoids: Pigments of Photosynthetic Biomembranes. . Methods in Enzymology, 148, 350-382.
 
Liebisch, F., N. Kirchgessner, D. Schneider, A. Walter, and A. Hund. 2015. Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods, 11(1): 9. http://dx.doi.org/10.1186/s13007-015-0048-8.
Lillian, B. (2016). senseFly’s Latest Fixed-Wing Mapping Drone .
Mekonnen, M. M., and A. Y. Hoekstra. 2016. Four billion people facing severe water scarcity. Science Advances, Vol. 2, no. 2, e1500323.
Moran, J. A., A. K. Mitchell, G. Goodmanson, and K. A. Stockburger. 2000. Differentiation among effects of nitrogen fertilization treatments on conifer seedlings by foliar reflectance: a comparison of methods. Tree Physiology 20, 1113–1120.
Nonami, K. 2010. Fundamental Modeling and Control of Small and Miniature Unmanned Helicopters. Springer, Autonomous Flying Robots: Unmanned Aerial Vehicles and Micro Aerial Vehicles.
Norton, G. J., M. Shafaei, A. J. Travis, C. M. Deacon, J. Danku, D. Pond, N. Cochrane, K. Lockhart, D. Salt, H. Zhang, I. C. Dodd, M. Hossain, M. R. Islam, and A. H. Price. 2017. Impact of alternate wetting and drying on rice physiology, grain production, and grain quality. Field Crops Research, 205: 1-13.
Parry, C., M. J. Blonquist, and B. Bugbee. 2014. In situ measurement of leaf chlorophyll concentration: analysis of the optical/absolute relationship. Plant, Cell and Environment (2014) 37, 2508–2520.

Pascual, V. J., and Y.-M. Wang. 2016. Utilizing rainfall and alternate wetting and drying irrigation for high water productivity in irrigated lowland paddy rice in southern Taiwan. Plant Production Science, 20(1): 24-35.
Patane, P., and V. Anup. 2014. Chlorophyll and Nitrogen Estimation Techniques: A Review. International Journal of Engineering Research and Reviews, Vol. 2(Issue 4): pp: (33-41).
Peñuelas, J., and I. Filella. 1998. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science 3: 151–156.
Porra, R. J. 2002. The chequered history of the development and use of simultaneous equations for the accurate determination of chlorophylls a and b. Photosynthesis Research 73:149–156.
Primicerio, J., S. F. Di Gennaro, E. Fiorillo, L. Genesio, E. Lugato, A. Matese, and F. P. Vaccari. 2012. A flexible unmanned aerial vehicle for precision agriculture Precision Agric (2012) 13:517–523.
Ramalho de Oliveira, L. F., M. L. Romarco de Oliveira, F. S. Gomes , and R. C. Santana. 2016. Estimating foliar nitrogen in Eucalyptus using vegetation indexes. Scientia Agricola.
Rawlings, J. O., S. G. Pantula, and D. A. Dickey. 1998. Applied Regression Analysis: A Research Tool, Second Edition (Springer Ed.).
 
Ritchie, R. J. 2006. Consistent Sets of Spectrophotometric Chlorophyll Equations for Acetone, Methanol and Ethanol Solvents. Photosynthesis Research, 89(1): 27-41.http://dx.doi.org/10.1007/s11120-006-9065-9.
Rokhmana, C. A. 2015. The Potential of UAV-based Remote Sensing for Supporting Precision Agriculture in Indonesia. Procedia Environmental Sciences, 24: 245 253.
Rouse, J. W., Jr., R. H. Haas, J. A. Schell, and D. W. Deering. 1979. Monitoring vegetation systems in the Great Plains with ERTS. NASA. Goddard Space Flight Center 3d ERTS-1 Symp., Vol. 1, Sect. A; p 309-317.
Shibghatallah, M. A. H., S. N. Khotimah, S. Suhandono, S. Viridi, and T. Kesuma. 2013. Measuring Leaf Chlorophyll Concentration from Its Color A Way in Monitoring Environment Change to Plantations. arXiv:1305.1148v2 [physics.bio-ph].
Sid'ko, A. F., I. Y. Botvich, T. I. Pisman, and A. P. Shevyrnogov. 2017. Estimation of chlorophyll content and yield of wheat crops from reflectance spectra obtained by ground-based remote measurements. Field Crops Research, 207: 24-29.
Sims, D. A., and J. A. Gamon. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81(2–3): 337-354.
 
Steele, M. R., A. A. Gitelson, and D. C. Rundquist. 2008. A Comparison of Two Techniques for Nondestructive Measurement of Chlorophyll Content in Grapevine Leaves. Agronomy Journal, Volume 100, Issue 3.
Sumanta, N., C. I. Haque, J. Nishika, and R. Suprakash. 2014. Spectrophotometric Analysis of Chlorophylls and Carotenoids from Commonly Grown Fern Species by Using Various Extracting Solvents. Research Journal of Chemical Sciences, Vol. 4(9)( ): (63-69).
Tahar, K. N., and A. Ahmad. 2013. An Evaluation on Fixed Wing and Multi-Rotor UAV Images using Photogrammetric Image Processing. International Journal of Computer, Electrical, Automation, Control and Information Engineering, Vol:7, No:1.
Tong, A., and Y. He. 2017. Estimating and mapping chlorophyll content for a heterogeneous grassland: Comparing prediction power of a suite of vegetation indices across scales between years. ISPRS Journal of Photogrammetry and Remote Sensing, 126: 146-167.
Tucker, C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150. http://www.sciencedirect.com/science/article/pii/0034425779900130
Ustin, S. L., A. A. Gitelson, S. Jacquemoud, M. Schaepman, G. P. Asner, J. A. Gamon, and P. Zarco-Tejada. 2009. Retrieval of foliar information about plant pigment systems from high resolution spectroscopy. Remote Sensing of Environment, 113, Supplement 1: S67-S77.
 
Vincini, M., E. Frazzi, and P. D’Alessio. 2008. A broad-band leaf chlorophyll index at the canopy scale. Precision Agric (2008) 9:303–319.
Warren, G., and G. Metternicht. 2005. Agricultural applications of high-resolution digital multispectral imagery: Evaluating within-field spatial variability of canola (Brassica napus) in Western Australia. Photogrammetric Engineering and Remote Sensing, 71 595–602
Wood, C. W., D. W. Reeves, and D. G. Himelrick. 1993. Relationships between chlorophyll meter readings and leaf chlorophyll concentration, N status, and crop yield: A review. 9 pages.
Wright, S. W., S. W. Jeffrey, and F. R. C. Mantoura. 1997. Evaluation of methods and solvents for pigment analysis. In: Phytoplankton pigments in oceanography: guidelines to modern methods. UNESCO Publ., Paris: 261–282
Yan, L., Z. Gou, and Y. Duan. 2009. A UAV Remote Sensing System: Design and Tests. Springer Science.
Yang, C., J. H. Everitt, and J. M. Bradford. 2006. Comparison of QuickBird satellite imagery and airborne imagery for mapping grain sorghum yield patterns Precision Agric, (2006) 7:33–44.
Zarco-Tejada, P. J. 2000. Hyperspectral Remote Sensing of Closed Forest Canopies Estimation of Chlorophyll Fluorescence and Pigment Content. Graduate Program in Earth and Space Science. York University Toronto, Ontario, Canada: 223 Pages. 
Zhang, C., and J. M. Kovacs. 2012. The application of small unmanned aerial systems for precision agriculture: a review. Precis Agric, 13. http://dx.doi.org/10.1007/s11119-012-9274-5.